Spacecraft are required to achieve fast attitude reorientation in many space missions. However, the attitude maneuver will be limited by the bounded and pointing constraints. The presence of these complex multiaxis constraints will largely reduce the feasible attitude space and greatly degrade the solving efficiency of the planning algorithm. For the time-optimal spacecraft reorientation problem under multiaxis constraints, a new heuristic decomposition planning on the virtual domain (HDPV) method is proposed in this paper. The parameterized attitude path is first determined on the virtual domain. Then the virtual-domain-based variable-step approach is presented to rapidly check the pointing constraints and generate the sets of candidate rotational-path decomposition nodes. The composite-position heuristics is developed to select the best node during the decomposition optimization, which considers the maneuver time and connection smoothness of paths. The total continuous parameterized path under keep-out and keep-in constraints is yielded through recursive planning. Finally, the time-optimal path parameterization approach is adopted to minimize the maneuver time along the total path and generate a fast attitude trajectory under bounded constraints on the time domain. Simulation results demonstrate the high computational efficiency and good optimization effect of the proposed method.

在许多太空任务中，航天器都需要实现快速的姿态重新定向。然而，姿态机动将受到有界和指向约束的限制。这些复杂的多轴约束的存在将大大减少可行的姿态空间，大大降低规划算法的求解效率。针对多轴约束下的时间最优航天器重定向问题，提出了一种新的虚拟域启发式分解规划（HDPV）方法。首先在虚拟域上确定参数化姿态路径。然后提出了基于虚拟域的可变步长方法来快速检查指向约束并生成候选旋转路径分解节点集。在分解优化过程中，开发了复合位置启发式算法来选择最佳节点，该算法考虑了路径的机动时间和连接平滑度。在keep-out和keep-in约束下的总连续参数化路径是通过递归规划产生的。最后，采用时间最优路径参数化方法，最大限度地减少沿总路径的机动时间，并在时域有界约束下生成快速姿态轨迹。仿真结果表明，该方法计算效率高，优化效果好。采用时间最优路径参数化方法，最大限度地减少沿总路径的机动时间，并在时域有界约束下生成快速姿态轨迹。仿真结果表明，该方法计算效率高，优化效果好。采用时间最优路径参数化方法，最大限度地减少沿总路径的机动时间，并在时域有界约束下生成快速姿态轨迹。仿真结果表明，该方法计算效率高，优化效果好。